Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2023 Feb 14;23(4):2155. doi: 10.3390/s23042155.
The event camera efficiently detects scene radiance changes and produces an asynchronous event stream with low latency, high dynamic range (HDR), high temporal resolution, and low power consumption. However, the large output data caused by the asynchronous imaging mechanism makes the increase in spatial resolution of the event camera limited. In this paper, we propose a novel event camera super-resolution (SR) network (EFSR-Net) based on a deep learning approach to address the problems of low spatial resolution and poor visualization of event cameras. The network model is capable of reconstructing high-resolution (HR) intensity images using event streams and active sensor pixel (APS) frame information. We design the coupled response blocks (CRB) in the network that are able of fusing the feature information of both data to achieve the recovery of detailed textures in the shadows of real images. We demonstrate that our method is able to reconstruct high-resolution intensity images with more details and less blurring in synthetic and real datasets, respectively. The proposed EFSR-Net can improve the peak signal-to-noise ratio (PSNR) metric by 1-2 dB compared with state-of-the-art methods.
事件相机能够有效地检测场景亮度变化，并生成具有低延迟、高动态范围 (HDR)、高时间分辨率和低功耗的异步事件流。然而，由于异步成像机制导致的大量输出数据，使得事件相机的空间分辨率提高受到限制。在本文中，我们提出了一种基于深度学习的新型事件相机超分辨率 (SR) 网络 (EFSR-Net)，以解决事件相机空间分辨率低和可视化效果差的问题。该网络模型能够使用事件流和主动传感器像素 (APS) 帧信息重建高分辨率 (HR) 强度图像。我们在网络中设计了耦合响应块 (CRB)，能够融合两种数据的特征信息，实现真实图像阴影中细节纹理的恢复。我们分别在合成和真实数据集上的实验结果表明，我们的方法能够重建具有更多细节和更少模糊的高分辨率强度图像。与最先进的方法相比，所提出的 EFSR-Net 可以将峰值信噪比 (PSNR) 度量提高 1-2dB。
